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An integrative approach to quantifying the effects of disturbance on regional forest carbon cycling 1. Introduction

Globally, forests play an important role in the terrestrial carbon cycle and are impacted by factors ranging from anthropogenic increases in atmospheric CO2 concentrations to altered disturbance regimes. Variations in forest insect pest disturbances have been attributed to the introduction of exotics (Liebhold et al. 1995) or changing ranges of natural pests with climate change (Volney and Fleming 2000). The U.S. Department of Agriculture and Forest Service reported 1-5 million ha of insect- and disease-caused tree mortality during 1997-2003 (USDA Forest Service, 2004). Compared to other forest disturbances, insects and disease influence the largest area of forests in the U.S. and Canada with economic costs over $1.5 billion (Dale et al. 2001).

Forest disturbance is one of the primary factors controlling regional carbon (C) fluxes (Li et al. 2003) especially net primary productivity (NPP), the net rate of absorption of atmospheric CO2 by terrestrial vegetation. Quantifying terrestrial NPP has been a major challenge in monitoring variations in the global C cycle as the complex feedback mechanisms and interactions between climate variability, nitrogen cycling, disturbance regime, and vegetation dynamics make understanding the short- to long-term influence forests have on the global C cycle difficult (Gruber and Galloway 2008).

Remote sensing approaches offer the potential to estimate the landscape- to regional-scale fluxes of C in the world’s forests, as well as other important constituents required for forest function (Smith et al. 2002). Remote sensing of NPP offers timely and repeated assessment of the health of forests as they undergo changes (i.e. succession, management) as well as rapid, and broad-scale appraisal of the effect of disturbances on the landscape. This proposed project uses remote sensing to (1) detect insect disturbance, and (2) quantify the landscape variation in light-use efficiency (ε); a key component in remote sensing of terrestrial NPP (Field et al. 1995; Monteith 1972) based on the functional and morphological properties of the vegetation. This information will be used to improve regional estimates forest NPP in the Upper Midwest region by investigating the interaction of climate and insect disturbance on inter-annual forest C cycling and storage using a satellite-driven production efficiency model (PEM).

1.2 Research objectives and hypotheses. Many challenges remain in identifying ecosystem consequences across perturbations, and in the context of global change. I pose the following questions for my research: i) How do forest ecosystems in the Upper Midwest respond to both climate variability and disturbance?, ii) How does the spatial variability in plant function influence the broad patterns of ecosystem productivity?, iii) How does disturbance intensity, as detected with remote sensing, influence recovery and forest carbon storage?, and iv) How can responses of forest ecosystems to disturbance and environmental change be most effectively characterized using an integrated remote sensing-modeling approach? The general idea is that, for a given perturbation (e.g. atmospheric nitrogen deposition, wildfire, insect defoliation), spatial variability in ecosystem response (e.g. forest productivity, nitrogen retention in the system) can be characterized using remotely-sensed data.

Hypothesis 1.1: Canopy nitrogen concentration will be greatest in the undisturbed forests compared to insect perturbed patches. Hypothesis 1.2: Insect activity will reduce the maximum ε and the magnitude of the reduction will depend on defoliation intensity.

Hypothesis 2.1: Annual NPP will be lower in the recently disturbed forest patches relative to the undisturbed forests. Hypothesis 2.2: Defoliation intensity will influence inter-annual NPP greater than climate variability.

Although some of these hypotheses have been demonstrated at the tree or plot level, no studies to date have addressed these issues across broad regions using a remotely-sensed approach.

Objective 1. Quantify landscape variation in ε of the vegetation using hyperspectral remote sensing data and field measurements

Objective 2. Quantify regional scale NPP, comparing and contrasting the spatio-temporal differences between insect disturbed and undisturbed forests

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2. Technical approach and rationale 2.1 Study region. The proposed study area is in the Upper Midwest (UM), in NW Wisconsin and NE Minnesota (Figure 1). Within this region, the Townsend and Mladenoff research groups at UW-Madison and B. Sturtevant at the USFS Northern Research Station have an extensive set of field (e.g. forest inventory, structural attributes, canopy chemistry, insect damage) and remotely-sensed data (e.g. AVIRIS, Hyperion, ASTER, Landsat, SPOT, and ALOS-PALSAR) on which my research is leveraged. I will specifically study forests dominated by spruce and firs (affected by spruce budworm, Choristoneura fumiferana), jack pine (affected by jack pine budworm, Choristoneura pinus pinus), and aspen (affected by forest tent caterpillar, Malacosoma disstria). 2.2 Field measurements. Existing measurements will be supplemented to include leaf area index (LAI; m2 m-2 per horizontal datum), fraction of photosynthetically active radiation (FPAR, 400-700nm) absorbed by the vegetation and fractional cover (%), as estimated using hemispherical photographs with CANEYE (Jonckheere et al. 2004). Additional measurements of stand structural and foliar characteristics (e.g. basal area by species, and foliar nitrogen concentration) will also be collected. I will utilize a plot sampling design suitable for multi-scale (e.g. plot to landscape) integration of field data with multi-resolution remotely-sensed data, which has been proven effective in previous research (Townsend 2000). All measurements will be made midsummer (late June – early August), when plant nutrient status and LAI are most stable (Martin and Aber 1997). These data will be related empirically to the hyperspectral data (Obj.1) to map variations in canopy nitrogen concentration, LAI, and FPAR to landscape patterns of ε.

2.2.1 Measures of canopy biochemistry. In addition to disturbance (Bond-Lamberty et al. 2007), canopy nitrogen (canopy N) is an important control in the variability of forest C cycling (Smith et al. 2002). Growing season canopy N will be calculated from bottom-, mid-, and top-of-canopy foliage (i.e. sun and shade leaves) samples, using a shotgun (Townsend et al. 2003). Current year and previous year’s foliage for evergreen conifer species will be sampled to ensure mean canopy N is more representative of the entire plot. Samples will be returned to the lab and processed using an Elementar Vario MACRO CHN analyzer. Total canopy N concentration for each field plot will be estimated by weighting the foliar N estimates for each species on the plot by its relative basal area (Townsend et al. 2003). The data will be used to scale from the plot to landscape-level (section 2.3.2) for use in refining estimates of forest NPP (section 2.4). 2.2.2 Plot-level productivity. Aboveground net primary productivity (ANPP) will be measured at an independent subsample of “intensive” plots as the sum of wood plus foliar production. Variable radius subplots will be used, at plot center and in the four cardinal directions. In the fall, tree diameter at breast height (DBH, 1.37 m) will be measured and annual radial increment will be quantified using scanned DBH tree cores taken at 90° from each tree greater than 2.5 cm, identified using basal area prisms (metric BAF 2, ensuring a minimum of ten trees per plot). The scanned radial wood increments will be measured using WinDENDRO® software (Regent Instruments, 2001). Annual measurements of tree DBH and radial increment data will be used to quantify the plot-level NPP using standard techniques and published allometric equations (Jenkins et al. 2003). Foliage NPP (NPPF) will be quantified using leaf litter-fall baskets placed in an “X” pattern, one at center and four at 10-m from plot center. Understory vegetation (NPPU) will be measured in the summer using clip plots. The dried (at 70° C for 72 hours) NPPF and NPPU samples will be weighed to calculate production on an annual basis. Total plot-level NPP (NPPT) will be estimated as the sum of ANPP and belowground NPP (NPPB, coarse and fine roots), calculated using below/above NPP coefficients of 0.2 and 0.4 for deciduous and coniferous dominated plots, respectively (Gower et al. 1999). These data will be used to validate the proposed remotely-sensed driven NPP model in Obj. 2 (section 2.4). All equipment needed for the proposed research is available from the UW Forest and Wildlife Ecology department. 2.3 Production efficiency models (PEMs). Vegetation requires radiation to convert atmospheric CO2 into essential organic compounds for growth and maintenance; as such, the amount of light absorbed by a plant

Figure 1. MODIS surface reflectance image for the Upper Midwest study area (orange rectangle).

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community is a major determinant of production. Monteith (1972, 1977) described the positive linear correlation between NPP and absorbed photosynthetically active radiation (APAR). In production efficiency modeling (PEM), the slope of this relationship is governed by the maximum or potential light-use efficiency (ε) term, i.e. the potential amount of atmospheric carbon fixed per unit of APAR, accounting for all photosynthetic and respitory processes (Eq. 1):

ε×= APARNPP Eq. 1 From a remote sensing standpoint APAR is calculated as APAR = FPAR x PAR, where FPAR is the

fractional scalar expressing the light harvesting potential of the vegetation (Gower et al. 1999). FPAR is commonly derived using relationships between FPAR and a remotely-sensed vegetation index (VI) (Myneni and Williams 1994). PAR is the incident photosynthetically active radiation, measured in-situ or estimated using satellite observations (e.g. Fensholt et al. 2006). 2.3.1 Light-use efficiency. PEMs have been utilized in numerous studies examining variations in terrestrial carbon sources and sinks (e.g. Goetz et al. 2000). Given their broad spatio-temporal coverage, computational economics, and practicality, PEMs may have the most potential to address the dynamics of ecosystem productivity at multiple scales. However, PEMs are generally difficult to parameterize for broad regions due to uncertainties in meteorological data (Zhao et al. 2006) and assignment of the light-use efficiency (ε) term, as it can vary considerably across species, functional groups, and ecosystems (Gower et al. 1999), requiring a more mechanistic approach (Goetz et al. 2000).

There has been substantial research into the remote detection of ε with the development of the PRI, photochemical reflectance index (Gamon et al. 1997). This index has been shown to track changes in ε from the leaf (e.g. Gamon et al., 1997) to ecosystem scales (e.g. Nichol et al. 2002), and serve as an estimate of ε to examine the sensitivity of forest NPP to drought stress (Asner et al. 2004). However, uncertainty remains in the relationship between PRI and ε (Grace et al. 2007) requiring further refinement. Recently, other methods which are strictly based on remote sensing data also show promise (e.g. Sims et al. 2007) yet still fail to provide a complete solution for all vegetation types.

Parameterization of PEMs could be improved by exploiting the relationship between plant productivity and absorbed radiation, which has been shown to be largely a function of the optimization of canopy N allocation (Goetz and Prince 1999; Medlyn 1998). In this vein, I propose to use information on the landscape variation in canopy N to illustrate the linkage between production and APAR, while avoiding issues related to PRI. I will further illustrate the controls of vegetation dynamics on carbon storage at a regional scale using spatially explicit estimates of ε. 2.3.2 Calculating landscape variation in ε. Objective 1. Quantify landscape variation in ε of the vegetation using hyperspectral remote sensing data and field measurements. The methods for deriving canopy N maps from AVIRIS and Hyperion are described in detail elsewhere (e.g. Bolster et al., 1996; Coops et al. 2003; Townsend et al. 2003) and are only briefly summarized here. Calibrated hyperspectral images will be converted to top-of-canopy reflectance using ACORN (ImSpec LLC, 2006) in conjunction with empirical line correction based on in-situ measurements of stable reflectors (i.e. parking lots). Partial least-squares regression, a type of eigenvector analysis, will be used to relate plot-level canopy N to the first-derivative transformed hyperspectral data. The accuracy of the resulting canopy N maps will be assessed quantitatively using an iterative cross-validation procedure to generate the residual mean square error of prediction (RMSEP) which is the average prediction error expressed in units of N concentration (e.g. gN 100 g-1 dry matter).

Canopy N for existing hyperspectral data (2005-2006) will be estimated using the generalized method for a wide range of forest ecosystems (Martin et al. 2008). In addition, the Townsend lab has proposed new acquisitions of AVIRIS and Hyperion data for 2008 and beyond, which will be used if available. If no new hyperspectral images are acquired then the analysis will focus on the 2005-2006 period.

How does the spatial variability in plant function influence the broad patterns of ecosystem productivity? Variations in canopy N may integrate the underlying factors influencing forest function (Ollinger and Smith 2005) and therefore carbon uptake (Magnani et al. 2007). By explicitly accounting for the spatial patterns of canopy N, remote sensing-based estimates of productivity should be improved (Smith et al. 2002). Using canopy N maps derived from the hyperspectral imagery, I will calculate the

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epsilon index (εindex) following Green et al. (2003). The εindex was shown to be a strong predictor of maximum instantaneous light-use efficiency (ε) for a variety of C3 plants, by integrating the relationship between canopy N (i.e. photosynthetic capacity) and morphological attributes into a single index value. The εindex will be calculated on a per-pixel basis following Eq. 2:

[ ] FPARLNmassindex ×= 100/ε Eq. 2

where the mapped canopy N (Nmass), LAI (L) and FPAR data are used. The landscape variation in LAI and FPAR will be mapped using derived relationships between field measurements and spectral reflectance data. Pixel-wise ε values will be estimated using the predictive equation ε = 1.54(εindex)

0.7 – 0.25 (Green et al. 2003). The value to this approach is that it avoids the use of species specific ε estimates by synthesizing plant functional properties according to nitrogen concentration and morphological characteristics. 2.3.3 Effect of disturbance on ε.

How does disturbance intensity, as detected with remote sensing, influence recovery and forest carbon storage? Following defoliation, there is a subsequent reduction in canopy N and diminished vegetation growth vigor (McNeil et al. 2007). This loss in canopy N and presumed reduction in ε, will be characterized using subsequent re-mapping of the vegetation with hyperspectral imagery (section 2.3.2) and/or Landsat and MODIS based defoliation indices (McNeil et al. 2007; Townsend et al. 2004). The objective is to relate changes in forest function (i.e. ε) to disturbance intensity to compare productivity of the perturbed and undisturbed vegetation and recovery, independent of climate variability.

2.4 Modeling approach. Objective 2: Quantify regional scale NPP, comparing and contrasting the spatio-temporal differences between disturbed and undisturbed forests.

How do forest ecosystems in the Upper Midwest region respond to both climate variability and disturbance? Climate variability and disturbance influences vegetation patterns and forest dynamics in the Upper Great Lakes region (Scheller and Mladenoff 2005), and the effects of disturbance may be as important as the effects of climate change alone on forest composition (important control over pest dispersal) and forest C fluxes (Schimel et al. 2001). While undisturbed forests generally contain large C pools, as forests age and with disturbance, the capacity for additional net uptake declines, which further reduces net ecosystem (NEP) C storage (Kurz and Apps 1993, 1999). Thus, defoliation-related decreases in productivity and the propensity for increased mortality can result in elevated surface fuel loading, resulting in greater risk of severe fires (Stocks 1987). Because of these complex interactions and heterogeneous processes driving the fluxes of C on the landscape, a variety of tools, including remote sensing and modeling, are required to fully understand defoliation impacts on the C dynamics of Upper

Midwest forests at short- to long-term scales. I propose to use a PEM approach, utilizing spatially explicit estimates of ε (sections 2.3.2 & 2.3.3) to quantify inter-annual NPP of disturbed and undisturbed forests.

The proposed PEM further incorporates physical constraints on the maximum quantum yield by scaling potential light-use efficiency (ε0) as a function of temperature, water and phenology regulation:

)()()(0 PfWfTfn ×××= εε Eq. 3

where ε0 is the potential light-use efficiency (section 2.3.2) and εn is the realized net light-use efficiency determined by the daily environmental limits to production from temperature (T), water availability (W), and phenology (P), i.e. vegetation seasonality and leaf longevity. Conceptually, ε0 is modified according to external scalars prior to estimation of daily NPP (Figure 2) at 250-m resolution. 2.4.1 Remotely-sensed data. Calculation of NPP is only meaningful for the active growing season period (White and Nemani 2003), which will be determined using daily time-series of MODerate resolution Imaging Spectroradiometer (MODIS) data. Following an approach similar to White et al., (1997), I will define Pscalar from 0 (min) to 1 (max) based on a daily surface reflectance vegetation index (VI) ratio. FPAR for the model will also be derived on a daily basis at 250-m resolution, using a MODIS VI, such as

Figure 2. Conceptual flow of the research

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the normalized difference vegetation index (NDVI), following methods published in the literature (e.g. Goward and Huemmrich 1992; Myneni and Williams 1994).

For estimation of daily incident PAR, high temporal resolution GOES data collected by the CIMSS facility at the UW-Madison will be used (http://www.soils.wisc.edu/wimnext/sun.html). Daily solar insolation is calculated from about 8-12 individual GOES and/or METEOSAT-7 images per day, corrected for cloudiness (Diak et al. 1996). A conversion factor of 0.47 relating daily total solar radiation to PAR will be used. This dataset was used in a pilot study and was found to be highly correlated (R2=0.91, p<0.0001) with PAR measurements at the Willow Creek, WI (WC) eddy covariance flux tower site.

The effects of water limitation on plant photosynthesis may be inferred from satellite observations in wavelengths sensitive to canopy water content (Hunt and Rock 1989; Xiao et al. 2004), especially in the shortwave infrared (SWIR) bands. The normalized difference infrared index NDII (Jackson et al. 2004) will be calculated from daily time-series MODIS data to be used as the water scalar (W, Eq. 3). 2.4.1 Climate data. Temperature will be derived from a gridded meteorological database, developed using individual station observations located throughout Wisconsin and Northern Minnesota (Serbin and Kucharik In review). This dataset consists of validated daily gridded maximum (Tmax) and minimum (Tmin) temperatures, as well as precipitation (PTotal) at 8-km resolution. In a pilot study (Serbin, unpub. data), daily average temperatures derived from these data were highly correlated (R2=0.94, p < 0.0001) with observations from the WC flux tower (45.81°N, -90.08°W).

The temperature scalar (Tscalar) for the proposed model is calculated each day following the method developed for the TEM, Terrestrial Ecosystem Model (Raich et al. 1991):

2maxmin

maxmin

)()]()[(

)()(

optAvgAvgAvg

AvgAvgscalar TTTTTT

TTTTT

−−−×−−×−

= Eq. 4

where TAvg is the average daily air temperature for each pixel and Tmin, Tmax, and Topt are the minimum, maximum, and optimum temperatures for photosynthesis, respectively. Tscalar will be parameterized with relevant Tmin, Tmax, and Topt values for these northern forests (Aber and Federer 1992; Raich et al. 1991). 2.5 Quantifying landscape variation in NPP. Insect outbreaks can have drastic influences on forest N

and C cycling (Kurz and Apps 1999), and pest outbreaks present a significant management challenge in the Lake States. I propose to extend the diagnostic PEM approach using spatially explicit estimates of ε to quantify the influences of climate variation and disturbance on forest NPP. How can responses of forest ecosystems to disturbance and drivers of environmental change be most effectively characterized using integrated remote sensing-modeling approach? In a pilot study (Serbin, unpub. data), the 2005 growing season NPP at the WC flux tower (Figure 3) was estimated using the proposed approach (section 2.4). The gridded climate data (section 2.4.1) was used for the climatic down-regulators. Tower NPP was calculated as 0.47GPPNPP ×= (Waring et al., 1998). With the

inclusion of spatially explicit values of ε, a more thorough understanding of the landscape patters in NPP can be quantified, especially as related to defoliation. 3. Scientific merit, application and deliverables

This research parallels ongoing work in the Townsend and Mladenoff labs using mechanistic models to estimate insect impacts on forest NPP. I propose an integrated approach using imaging spectroscopy (to quantify canopy N), Landsat (to estimate disturbance) and MODIS (to track disturbance and also to drive the daily NPP model). Forest function (i.e. ε) will be assessed by uniting spectroscopy and ecophysiology to estimate an important vegetation parameter that governs C exchange. The value to this approach is that it avoids using species specific ε estimates by synthesizing plant functional properties according to nitrogen concentration and morphological characteristics. Results will be presented at scientific conferences and distributed via the internet, and shared with government agencies and regional conservation groups. Multiple peer-reviewed publications are planned for submission to high-quality remote sensing and ecology journals (e.g. Rem. Sens. Env., IEEE Trans. Geosci. Rem. Sens., Ecol. Apps.).

Figure 3. Observed and predicted daily NPP at the WC flux tower.

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The ability to seamlessly scale information on forest function across a continuum of scales, from field to satellite observations, greatly enhances our ability to understand how the terrestrial vegetation-atmosphere interactions change over time in response to anthropogenic and natural disturbances. My proposed research will advance the knowledge and understanding of how forest functioning responds directly and indirectly to disturbance and inter-annual climate variability while enhancing our ability to scale forest processes to more coarse scale analyses (e.g. plot to Landsat to MODIS). These questions are directly applicable to NASA’s study of terrestrial carbon cycling (e.g. NACP), Plant Physiology and Functional Types (PPFT) Mission Concept Study, as well as Terrestrial Ecology and LCLUC Programs. Results from this research will also provide a significant step forward in the further development and use of proficient satellite-driven productivity models. Management of forests and the monitoring of ecosystem health following disturbance, such as defoliation, may be vastly improved using these proposed remote sensing techniques to gather information on inter-annual forest functioning. 4. References Aber, J.D., et al. (1992). Oecologia, 92, 463-474 Asner, G.P., et al. (2004). Proceedings of the National Academy of Sciences of the United States of America, 101, 6039-6044 Bond-Lamberty, B., et al. (2007). Nature, 450 Bolster, KL et al., (1996) Canadian Journal of Forest Research, 26, 590-600 Coops, N.C., et al. (2003). Ieee Transactions on Geoscience and Remote Sensing, 41, 1338-1346 Dale, V.H., et al. (2001). Bioscience, 51, 723-734 Diak, G.R., et al. (1996). Agricultural and Forest Meteorology, 82, 219-226 Fensholt, R., et al. (2006). Remote Sensing of Environment, 105, 173-188 Field, C.B., et al. (1995). Remote Sensing of Environment, 51, 74-88 Gamon, J.A., et al. (1997). Oecologia, 112, 492-501 Goetz, S.J., et al. (1999).In, Advances in Ecological Research, Vol 28 (pp. 57-92). London: Academic Press Ltd Goetz, S.J., et al. (2000). Journal of Geophysical Research-Atmospheres, 105, 20077-20091 Goward, S.N., et al. (1992). Remote Sensing of Environment, 39, 119-140 Gower, S.T., et al. (1999). Remote Sensing of Environment, 70, 29-51 Grace, J., et al. (2007). Global Change Biology, 13, 1484-1497 Green, D.S., et al. (2003). Agricultural and Forest Meteorology, 115, 165-173 Gruber, N., et al. (2008). Nature, 451, 293-296 Hunt, E.R., et al. (1989). Remote Sensing of Environment, 30, 43-54 Jackson, T.J., et al. (2004). Remote Sensing of Environment, 92, 475-482 Jenkins, J.C., et al. (2003). Forest Science, 49, 12-35 Jonckheere, I., et al. (2004). Agricultural and Forest Meteorology, 121, 19-35 Kurz, W.A., et al. (1993). Water Air and Soil Pollution, 70, 163-176 Kurz, W.A., et al. (1999). Ecological Applications, 9, 526-547 Li, Z., et al. (2003). Canadian Journal of Forest Research-Revue Canadienne De Recherche Forestiere, 33, 2340-2351 Liebhold, A.M., et al. (1995). Forest Science, 41, 1-49 Magnani, F., et al. (2007). Nature, 447, 848-850 Martin, M.E., et al. (1997). Ecological Applications, 7, 431-443 Martin, M.E., et al. (2008). Remote Sensing of Environment, IN REVISION McNeil, B.E., et al. (2007). Geophysical Research Letters, 34, 5 Medlyn, B.E. (1998). Tree Physiology, 18, 167-176 Monteith, J.L. (1972). Journal of Applied Ecology, 9, 747-766 Myneni, R.B., et al. (1994). Remote Sensing of Environment, 49, 200-211 Nichol, C.J., et al. (2002). Tellus Series B-Chemical and Physical Meteorology, 54, 677-687 Ollinger, S.V., et al. (2005). Ecosystems, 8, 760-778 Raich, J.W., et al. (1991). Ecological Applications, 1, 399-429 Scheller, R.M., et al. (2005). Global Change Biology, 11, 307-321 Schimel, D.S., et al. (2001). Nature, 414, 169-172 Serbin, S.P., et al. (In review). Agricultural and Forest Meteorology, Submitted Sims, D.A., et al. (2007). Remote Sensing of Environment, In press Smith, M.L., et al. (2002). Ecological Applications, 12, 1286-1302 Stocks, B.J. (1987). Forestry Chronicle, 63, 8-14 Townsend, P.A. (2000). Remote Sensing of Environment, 72, 253-267 Townsend, P.A., et al. (2004). Ecological Applications, 14, 504-516 Townsend, P.A., et al. (2003). Ieee Transactions on Geoscience and Remote Sensing, 41, 1347-1354 Volney, W.J.A., et al. (2000). Agriculture Ecosystems & Environment, 82, 283-294 Waring, R.H., et al. (1998). Tree Physiology, 18, 129-134 White, M.A., et al. (2003). Global Change Biology, 9, 967-972 Xiao, X.M., et al. (2004). Remote Sensing of Environment, 91, 256-270 Zhao, M., et al. (2006). Journal of Geophysical Research-Biogeosciences, 111, 13